Combining multiple Large Volume Metrology systems: Competitive versus cooperative data fusion

Large Volume Metrology (LVM) tasks can require the concurrent use of several measuring systems. These systems generally consist of set of sensors measuring the distances and/or angles with respect to a point of interest so as to determine its 3D position. When combining different measuring systems, characterized by sensors of different nature, competitive or cooperative methods can be adopted for fusing data. Competitive methods, which are by far the most diffused in LVM, basically perform a weighted mean of the 3D positions determined by the individual measuring systems. On the other hand, for cooperative methods, distance and/or angular measurements by sensors of different systems are combined together in order to determine a unique 3D position of the point of interest. This paper proposes a novel cooperative approach which takes account of the measurement uncertainty in distance and angular measurements of sensors of different nature. The proposed approach is compared with classical competitive approaches from the viewpoint of the metrological performance. The main advantages of the cooperative approach, with respect to the competitive one, are: (i) it is the only option when the individual LVM systems are not able to provide autonomous position measurements (e.g., laser interferometers or single cameras), (ii) it is the only option when only some of the sensors of autonomous systems work correctly (for instance, a laser tracker in which only distance – not angular – measurements are performed), (iii) when using systems with redundant sensors (i.e. photogrammetric systems with a large number of distributed cameras), point localization tends to be better than that using the competitive fusion approach

Cooperative Anchor-Free Position Estimation for Hierarchical Wireless Sensor Networks

This paper proposes a distributed algorithm for establishing connectivity and location estimation in cluster-based wireless sensor networks. The algorithm exploits the information flow while coping with distributed signal processing and the requirements of network scalability. Once the estimation procedure and communication protocol are performed, sensor clusters can be merged to establish a single global coordinate system without GPS sensors using only distance information. In order to adjust the sensor positions, the refinement schemes and cooperative fusion approaches are applied to reduce the estimation error and improve the measurement accuracy. This paper outlines the technical foundations of the localization techniques and presents the tradeoffs in algorithm design. The feasibility of the proposed schemes is shown to be effective under certain assumptions and the analysis is supported by simulation and numerical studies

Cooperative Anchor-Free Position Estimation for Hierarchical Wireless Sensor Networks

This paper proposes a distributed algorithm for establishing connectivity and location estimation in cluster-based wireless sensor networks. The algorithm exploits the information flow while coping with distributed signal processing and the requirements of network scalability. Once the estimation procedure and communication protocol are performed, sensor clusters can be merged to establish a single global coordinate system without GPS sensors using only distance information. In order to adjust the sensor positions, the refinement schemes and cooperative fusion approaches are applied to reduce the estimation error and improve the measurement accuracy. This paper outlines the technical foundations of the localization techniques and presents the tradeoffs in algorithm design. The feasibility of the proposed schemes is shown to be effective under certain assumptions and the analysis is supported by simulation and numerical studies